Combine BottomCount() with Other MDX Functions to Add Sophistication

Next, we need to add logic to generate the same “bottom four” data, but for an ancestor of City (another level in the Geography hierarchy of the Geography dimension), the State that the City inhabits. To do so, we will introduce more MDX functions, as we see in the next step.

-- MDX072-1-2, BottomCount(), combined with Ancestor()  
--   and .Name in a calculated member, to provide  
--   U. S. States with each of "Bottom 4 Least Performing    
--   (with regard to Total Reseller Sales Amounts) Cities 
--   in Adventure Works Organization in CY 2003"

WITH 
MEMBER
   [Measures].[State] 
AS
   'ANCESTOR([Geography].[Geography].CURRENTMEMBER,     
      [Geography].[Geography].[State-Province]).NAME'

This will create the calculated member State, which will allow us to present the States alongside their respective member Cities.

{[Measures].[State], [Measures].[Reseller Sales Amount]} ON AXIS(0),

The Query pane appears as depicted in Illustration 3, with our modifications marked.

The Results pane is populated, and the dataset shown in Illustration 4 appears.

We note that the State to which each of the “bottom four” Cities (still the bottom four for the entire Adventure Works organization, we also note) belongs appears in the results. Our calculated member has taken us a step closer to realization of the information consumers’ requirements.

At this point, all that remains is to generate the “bottom four” data for each of the U.S. States, limiting the States to the U.S. States within which Adventure Works reseller sales occur. The word “generate” is particularly apt here: we will use the Generate() function in our efforts to return the “bottom four” from each of the U.S. States involved.

-- MDX072-1-3, Addition of Generate() to retrieve 
--   4 Worst Performing Cities in Each U. S. State (with regard to 
--      total Reseller Sales Amounts) in CY 2003"

{GENERATE
    (
       [Geography].[Geography].[Country].[United States].CHILDREN,
           BOTTOMCOUNT(
              DESCENDANTS(
                 [Geography].[Geography].CURRENTMEMBER,
                     [Geography].[Geography].[City]   
                        ),
                     4,
                 [Measures].[Reseller Sales Amount])
              )
           }
        ON AXIS(1)

(In actuality, we are only adding the Generate() statement to the “front end” of the ON AXIS(1) specification, as it already existed from earlier steps, with a closing right parenthesis - “)” - just before the ON AXIS keyword).

The Generate() statement builds a set based upon the bottom performing Cities, as retrieved by the BottomCount() function (with Reseller Sales Amount as the <<Numeric Expression>> argument, and the Descendants() function, once again, specifying the «Set» argument as the individual Cities), limited to U.S. States (the “children” of the United States level of the Geography dimension / Geography hierarchy.

The Query pane appears as depicted in Illustration 5, with our additions marked.

The Results pane is populated, and the dataset partially shown in Illustration 6 appears.

Finally, we see the presentation as requested by the information consumers: CY 2003 total Reseller Sales Amounts for the four Cities with the lowest Reseller Sales Amounts - for each of the U. S. States within which Adventure Works has conducted Reseller Sales operations. We note that several Cities display “nulls” – this is appropriate in our simple example where, for instance, two of the four least performing” Cities in the State of Alabama appear in our dataset because they had no Reseller Sales activity, over the Calendar Year by which we have sliced within our query. (There are other ways we can refine the query for presentation purposes – say to show the “least four” Cities with non-zero values, etc., - but such refinements are beyond the scope of this article.)

Summary ...

This article served as an extension of our previous introductory article, Basic Set Functions: The BottomCount() Function. We briefly reviewed the operation of the BottomCount() function, and then extended our examination of BottomCount() to include a somewhat more sophisticated use.

We defined an illustrative business need, as posed to us by hypothetical groups of information consumers, and, after discussing the need in general, with regard to challenges inherent to meeting them with the BottomCount() function in particular. We then constructed, in a step-by-step manner, a final query to meet the expressed requirements using a combination of BottomCount() and other MDX functions we have explored in articles of the MDX Essentials series. Throughout our practice example, we discussed the syntax contained within the solutions we constructed for the information consumers, as well as the datasets we obtained in employing BottomCount() and other functions, together with other surrounding considerations.

About the MDX Essentials Series ...

This article is a member of the MDX Essentials series, a monthly column designed to provide hands-on application of the fundamentals of the Multidimensional Expressions (MDX) language, with each article progressively adding features designed to meet specific real-world needs.

For more information about the column in general, as well as the software and systems requirements for getting the most out of the lessons included, please see my first article, MDX at First Glance: Introduction to MDX Essentials, among others.